
import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ViTMAEForPreTraining, ViTMAEModel
						from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor
class 					lowercase__:



	"""simple docstring"""




	def __init__(			self    :     str			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=1_3			,				SCREAMING_SNAKE_CASE_    :     str=3_0			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=2			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=3			,				SCREAMING_SNAKE_CASE_    :     int=True			,				SCREAMING_SNAKE_CASE_    :     List[str]=True			,				SCREAMING_SNAKE_CASE_    :     str=3_2			,				SCREAMING_SNAKE_CASE_    :     Any=5			,				SCREAMING_SNAKE_CASE_    :     Any=4			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=3_7			,				SCREAMING_SNAKE_CASE_    :     List[Any]="gelu"			,				SCREAMING_SNAKE_CASE_    :     Dict=0.1			,				SCREAMING_SNAKE_CASE_    :     Any=0.1			,				SCREAMING_SNAKE_CASE_    :     str=1_0			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=0.02			,				SCREAMING_SNAKE_CASE_    :     str=3			,				SCREAMING_SNAKE_CASE_    :     int=0.6			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=None			,				)			->	str:
					lowercase_	    =   parent
					lowercase_	    =   batch_size
					lowercase_	    =   image_size
					lowercase_	    =   patch_size
					lowercase_	    =   num_channels
					lowercase_	    =   is_training
					lowercase_	    =   use_labels
					lowercase_	    =   hidden_size
					lowercase_	    =   num_hidden_layers
					lowercase_	    =   num_attention_heads
					lowercase_	    =   intermediate_size
					lowercase_	    =   hidden_act
					lowercase_	    =   hidden_dropout_prob
					lowercase_	    =   attention_probs_dropout_prob
					lowercase_	    =   type_sequence_label_size
					lowercase_	    =   initializer_range
					lowercase_	    =   mask_ratio
					lowercase_	    =   scope

					# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
					# (we add 1 for the [CLS] token)
					lowercase_	    =   (image_size // patch_size) ** 2
					lowercase_	    =   int(math.ceil((1 - mask_ratio) * (num_patches + 1)			)			)
	def 		_lowercase    (			self    :     List[Any]			)			->	Union[str, Any]:
					lowercase_	    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

					lowercase_	    =   None
					if self.use_labels:
									lowercase_	    =   ids_tensor([self.batch_size]			,				self.type_sequence_label_size			)

					lowercase_	    =   self.get_config()

					return config, pixel_values, labels
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	int:
					return ViTMAEConfig(
					    image_size=self.image_size			,				patch_size=self.patch_size			,				num_channels=self.num_channels			,				hidden_size=self.hidden_size			,				num_hidden_layers=self.num_hidden_layers			,				num_attention_heads=self.num_attention_heads			,				intermediate_size=self.intermediate_size			,				hidden_act=self.hidden_act			,				hidden_dropout_prob=self.hidden_dropout_prob			,				attention_probs_dropout_prob=self.attention_probs_dropout_prob			,				is_decoder=SCREAMING_SNAKE_CASE_			,				initializer_range=self.initializer_range			,				mask_ratio=self.mask_ratio			,				)
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     str			)			->	int:
					lowercase_	    =   ViTMAEModel(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size)			)
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			)			->	Optional[Any]:
					lowercase_	    =   ViTMAEForPreTraining(SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   (self.image_size // self.patch_size) ** 2
					lowercase_	    =   self.patch_size**2 * self.num_channels
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, num_patches, expected_num_channels)			)

					# test greyscale images
					lowercase_	    =   1
					lowercase_	    =   ViTMAEForPreTraining(SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   self.patch_size**2
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, num_patches, expected_num_channels)			)




	def 		_lowercase    (			self    :     Tuple			)			->	str:
					lowercase_	    =   self.prepare_config_and_inputs()
					lowercase_    ,      lowercase_    ,      lowercase_	    =   config_and_inputs
					lowercase_	    =   {'''pixel_values''': pixel_values}
					return config, inputs_dict

@require_torch
class 					lowercase__(							UpperCAmelCase					,		UpperCAmelCase					,		unittest.TestCase				):



	"""simple docstring"""




	a     :Tuple       	=      (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
	a     :List[str]       	=      {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

	a     :Any       	=      False
	a     :Optional[int]       	=      False
	a     :Union[str, Any]       	=      False
	a     :Any       	=      False
	def 		_lowercase    (			self    :     str			)			->	Optional[Any]:
					lowercase_	    =   ViTMAEModelTester(self			)
					lowercase_	    =   ConfigTester(self			,				config_class=SCREAMING_SNAKE_CASE_			,				has_text_modality=SCREAMING_SNAKE_CASE_			,				hidden_size=3_7			)
	def 		_lowercase    (			self    :     Dict			)			->	Optional[Any]:
					self.config_tester.run_common_tests()
	@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''			)
	def 		_lowercase    (			self    :     int			)			->	Union[str, Any]:
					pass
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	List[str]:
					lowercase_    ,      lowercase_	    =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									lowercase_	    =   model_class(SCREAMING_SNAKE_CASE_			)
									self.assertIsInstance(model.get_input_embeddings()			,				(nn.Module)			)
									lowercase_	    =   model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_			,				nn.Linear			)			)
	def 		_lowercase    (			self    :     int			)			->	Tuple:
					lowercase_    ,      lowercase_	    =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									lowercase_	    =   model_class(SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   inspect.signature(model.forward			)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									lowercase_	    =   [*signature.parameters.keys()]

									lowercase_	    =   ['''pixel_values''']
									self.assertListEqual(arg_names[:1]			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Optional[Any]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Tuple:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			)			->	List[str]:
					# make masks reproducible
					np.random.seed(2			)

					lowercase_	    =   int((pt_model.config.image_size // pt_model.config.patch_size) ** 2			)
					lowercase_	    =   np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)
					lowercase_	    =   torch.from_numpy(SCREAMING_SNAKE_CASE_			)

					# Add `noise` argument.
					# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
					lowercase_	    =   pt_noise

					super().check_pt_tf_models(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     str			)			->	Optional[Any]:
					lowercase_    ,      lowercase_	    =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									lowercase_	    =   model_class(SCREAMING_SNAKE_CASE_			)
									model.to(SCREAMING_SNAKE_CASE_			)
									model.eval()
									# make random mask reproducible
									torch.manual_seed(2			)
									with torch.no_grad():
													lowercase_	    =   model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			)

									lowercase_	    =   outputs[0].cpu().numpy()
									lowercase_	    =   0

									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(SCREAMING_SNAKE_CASE_			)
													lowercase_	    =   model_class.from_pretrained(SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													# make random mask reproducible
													torch.manual_seed(2			)
													with torch.no_grad():
																	lowercase_	    =   model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			)

													# Make sure we don't have nans
													lowercase_	    =   after_outputs[0].cpu().numpy()
													lowercase_	    =   0
													lowercase_	    =   np.amax(np.abs(out_a - out_a			)			)
													self.assertLessEqual(SCREAMING_SNAKE_CASE_			,				1e-5			)
	@unittest.skip(
	    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''			)
	def 		_lowercase    (			self    :     Dict			)			->	int:
					pass
	@unittest.skip(
	    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''			)
	def 		_lowercase    (			self    :     int			)			->	Optional[int]:
					pass
	@unittest.skip(
	    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	List[Any]:
					pass
	@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Union[str, Any]:
					pass
	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''			)
	def 		_lowercase    (			self    :     List[Any]			)			->	Optional[Any]:
					pass




	@slow
	def 		_lowercase    (			self    :     Dict			)			->	List[str]:
					for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_	    =   ViTMAEModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)




def 							a		(				):


				'''simple docstring'''
				lowercase_	    =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
				return image


@require_torch
@require_vision
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	@cached_property
	def 		_lowercase    (			self    :     Any			)			->	Dict:
					return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''			) if is_vision_available() else None




	@slow
	def 		_lowercase    (			self    :     Optional[int]			)			->	List[Any]:
					# make random mask reproducible across the PT and TF model
					np.random.seed(2			)

					lowercase_	    =   ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''			).to(SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   self.default_image_processor
					lowercase_	    =   prepare_img()
					lowercase_	    =   image_processor(images=SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''			).to(SCREAMING_SNAKE_CASE_			)

					# prepare a noise vector that will be also used for testing the TF model
					# (this way we can ensure that the PT and TF models operate on the same inputs)
					lowercase_	    =   ViTMAEConfig()
					lowercase_	    =   int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2			)
					lowercase_	    =   np.random.uniform(size=(1, num_patches)			)

					# forward pass
					with torch.no_grad():
									lowercase_	    =   model(**SCREAMING_SNAKE_CASE_			,				noise=torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_			)			)

					# verify the logits
					lowercase_	    =   torch.Size((1, 1_9_6, 7_6_8)			)
					self.assertEqual(outputs.logits.shape			,				SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   torch.tensor(
					    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]			)

					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]			,				expected_slice.to(SCREAMING_SNAKE_CASE_			)			,				atol=1e-4			)			)












"""simple docstring"""





import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


_SCREAMING_SNAKE_CASE   :							str          =		logging.getLogger(__name__)






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					"""summarization"""
    SCREAMING_SNAKE_CASE		:	Any		=					["""loss"""]
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					ROUGE_KEYS
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					"""rouge2"""






    def __init__(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		**__SCREAMING_SNAKE_CASE	:				List[str]   )	->						Union[str, Any]:
      if hparams.sortish_sampler and hparams.gpus > 1:
        lowerCamelCase_       	=		False
      elif hparams.max_tokens_per_batch is not None:
        if hparams.gpus > 1:
          raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training'   )
        if hparams.sortish_sampler:
          raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'   )

      super().__init__(__SCREAMING_SNAKE_CASE			,		num_labels=__SCREAMING_SNAKE_CASE			,		mode=self.mode			,		**__SCREAMING_SNAKE_CASE   )
      use_task_specific_params(self.model			,		'summarization'   )
      save_git_info(self.hparams.output_dir   )
      lowerCamelCase_       	=		Path(self.output_dir   ) / 'metrics.json'
      lowerCamelCase_       	=		Path(self.output_dir   ) / 'hparams.pkl'
      pickle_save(self.hparams			,		self.hparams_save_path   )
      lowerCamelCase_       	=		0
      lowerCamelCase_       	=		defaultdict(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.config.model_type
      lowerCamelCase_       	=		self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

      lowerCamelCase_       	=		{
          "data_dir": self.hparams.data_dir,
          "max_source_length": self.hparams.max_source_length,
          "prefix": self.model.config.prefix or "",
      }
      lowerCamelCase_       	=		{
          'train': self.hparams.n_train,
          'val': self.hparams.n_val,
          'test': self.hparams.n_test,
      }
      lowerCamelCase_       	=		{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

      lowerCamelCase_       	=		{
          'train': self.hparams.max_target_length,
          'val': self.hparams.val_max_target_length,
          'test': self.hparams.test_max_target_length,
      }
      assert self.target_lens["train"] <= self.target_lens["val"], F'''target_lens: {self.target_lens}'''
      assert self.target_lens["train"] <= self.target_lens["test"], F'''target_lens: {self.target_lens}'''
      if self.hparams.freeze_embeds:
        freeze_embeds(self.model   )
      if self.hparams.freeze_encoder:
        freeze_params(self.model.get_encoder()   )
        assert_all_frozen(self.model.get_encoder()   )

      lowerCamelCase_       	=		get_git_info()['repo_sha']
      lowerCamelCase_       	=		hparams.num_workers
      lowerCamelCase_       	=		None  # default to config
      if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer			,		__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		self.tokenizer.lang_code_to_id[hparams.tgt_lang]
        lowerCamelCase_       	=		self.decoder_start_token_id
      lowerCamelCase_       	=		(
          SeqaSeqDataset if hasattr(self.tokenizer			,		'prepare_seq2seq_batch'   ) else LegacySeqaSeqDataset
      )
      lowerCamelCase_       	=		False
      lowerCamelCase_       	=		self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
      if self.hparams.eval_max_gen_length is not None:
        lowerCamelCase_       	=		self.hparams.eval_max_gen_length
      else:
        lowerCamelCase_       	=		self.model.config.max_length
      lowerCamelCase_       	=		self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Dict[str, torch.Tensor]   )	->						Dict[str, List[str]]:
      lowerCamelCase_       	=		{
          k: self.tokenizer.batch_decode(v.tolist()   ) if 'mask' not in k else v.shape for k, v in batch.items()
      }
      save_json(__SCREAMING_SNAKE_CASE			,		Path(self.output_dir   ) / 'text_batch.json'   )
      save_json({k: v.tolist() for k, v in batch.items()}			,		Path(self.output_dir   ) / 'tok_batch.json'   )

      lowerCamelCase_       	=		True
      return readable_batch






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				int			,		**__SCREAMING_SNAKE_CASE	:				List[Any]   )	->						Any:
      return self.model(__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				List[int]   )	->						int:
      lowerCamelCase_       	=		self.tokenizer.batch_decode(
          __SCREAMING_SNAKE_CASE			,		skip_special_tokens=__SCREAMING_SNAKE_CASE			,		clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
      return lmap(str.strip			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Any			,		__SCREAMING_SNAKE_CASE	:				dict   )	->						Tuple:
      lowerCamelCase_       	=		self.tokenizer.pad_token_id
      lowerCamelCase_	,      lowerCamelCase_       	=		batch['input_ids'], batch['attention_mask']
      lowerCamelCase_       	=		batch['labels']
      if isinstance(self.model			,		__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		self.model._shift_right(__SCREAMING_SNAKE_CASE   )
      else:
        lowerCamelCase_       	=		shift_tokens_right(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
        lowerCamelCase_       	=		decoder_input_ids
        self.save_readable_batch(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self(__SCREAMING_SNAKE_CASE			,		attention_mask=__SCREAMING_SNAKE_CASE			,		decoder_input_ids=__SCREAMING_SNAKE_CASE			,		use_cache=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		outputs['logits']
      if self.hparams.label_smoothing == 0:
        # Same behavior as modeling_bart.py, besides ignoring pad_token_id
        lowerCamelCase_       	=		nn.CrossEntropyLoss(ignore_index=__SCREAMING_SNAKE_CASE   )

        assert lm_logits.shape[-1] == self.vocab_size
        lowerCamelCase_       	=		ce_loss_fct(lm_logits.view(-1			,		lm_logits.shape[-1]   )			,		tgt_ids.view(-1   )   )
      else:
        lowerCamelCase_       	=		nn.functional.log_softmax(__SCREAMING_SNAKE_CASE			,		dim=-1   )
        lowerCamelCase_	,      lowerCamelCase_       	=		label_smoothed_nll_loss(
            __SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		self.hparams.label_smoothing			,		ignore_index=__SCREAMING_SNAKE_CASE   )
      return (loss,)






    @property
    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						int:
      return self.tokenizer.pad_token_id






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Any   )	->						Dict:
      lowerCamelCase_       	=		self._step(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		dict(zip(self.loss_names			,		__SCREAMING_SNAKE_CASE   )   )
      # tokens per batch
      lowerCamelCase_       	=		batch['input_ids'].ne(self.pad   ).sum() + batch['labels'].ne(self.pad   ).sum()
      lowerCamelCase_       	=		batch['input_ids'].shape[0]
      lowerCamelCase_       	=		batch['input_ids'].eq(self.pad   ).sum()
      lowerCamelCase_       	=		batch['input_ids'].eq(self.pad   ).float().mean()
      # TODO(SS): make a wandb summary metric for this
      return {"loss": loss_tensors[0], "log": logs}






    def   UpperCamelCase			(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				int   )	->						Dict:
      return self._generative_step(__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]="val"   )	->						Dict:
      self.step_count += 1
      lowerCamelCase_       	=		{k: torch.stack([x[k] for x in outputs]   ).mean() for k in self.loss_names}
      lowerCamelCase_       	=		losses['loss']
      lowerCamelCase_       	=		{
          k: np.array([x[k] for x in outputs]   ).mean() for k in self.metric_names + ['gen_time', 'gen_len']
      }
      lowerCamelCase_       	=		(
          generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
      )
      lowerCamelCase_       	=		torch.tensor(__SCREAMING_SNAKE_CASE   ).type_as(__SCREAMING_SNAKE_CASE   )
      generative_metrics.update({k: v.item() for k, v in losses.items()}   )
      losses.update(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		{F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
      lowerCamelCase_       	=		self.step_count
      self.metrics[prefix].append(__SCREAMING_SNAKE_CASE   )  # callback writes this to self.metrics_save_path
      lowerCamelCase_       	=		flatten_list([x['preds'] for x in outputs]   )
      return {
          "log": all_metrics,
          "preds": preds,
          F'''{prefix}_loss''': loss,
          F'''{prefix}_{self.val_metric}''': metric_tensor,
      }






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				List[str]   )	->						Dict:
      return calculate_rouge(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				dict   )	->						dict:
      lowerCamelCase_       	=		time.time()

      # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
      lowerCamelCase_       	=		self.model.generate(
          batch['input_ids']			,		attention_mask=batch['attention_mask']			,		use_cache=__SCREAMING_SNAKE_CASE			,		decoder_start_token_id=self.decoder_start_token_id			,		num_beams=self.eval_beams			,		max_length=self.eval_max_length			,		)
      lowerCamelCase_       	=		(time.time() - ta) / batch['input_ids'].shape[0]
      lowerCamelCase_       	=		self.ids_to_clean_text(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.ids_to_clean_text(batch['labels']   )
      lowerCamelCase_       	=		self._step(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		dict(zip(self.loss_names			,		__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		self.calc_generative_metrics(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		np.mean(lmap(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )   )
      base_metrics.update(gen_time=__SCREAMING_SNAKE_CASE			,		gen_len=__SCREAMING_SNAKE_CASE			,		preds=__SCREAMING_SNAKE_CASE			,		target=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )
      return base_metrics






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				int   )	->						Any:
      return self._generative_step(__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						Tuple:
      return self.validation_epoch_end(__SCREAMING_SNAKE_CASE			,		prefix='test'   )






    def   UpperCamelCase			(			self	:				Any			,		__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						SeqaSeqDataset:
      lowerCamelCase_       	=		self.n_obs[type_path]
      lowerCamelCase_       	=		self.target_lens[type_path]
      lowerCamelCase_       	=		self.dataset_class(
          self.tokenizer			,		type_path=__SCREAMING_SNAKE_CASE			,		n_obs=__SCREAMING_SNAKE_CASE			,		max_target_length=__SCREAMING_SNAKE_CASE			,		**self.dataset_kwargs			,		)
      return dataset






    def   UpperCamelCase			(			self	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				bool = False   )	->						DataLoader:
      lowerCamelCase_       	=		self.get_dataset(__SCREAMING_SNAKE_CASE   )

      if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
        lowerCamelCase_       	=		dataset.make_sortish_sampler(__SCREAMING_SNAKE_CASE			,		distributed=self.hparams.gpus > 1   )
        return DataLoader(
            __SCREAMING_SNAKE_CASE			,		batch_size=__SCREAMING_SNAKE_CASE			,		collate_fn=dataset.collate_fn			,		shuffle=__SCREAMING_SNAKE_CASE			,		num_workers=self.num_workers			,		sampler=__SCREAMING_SNAKE_CASE			,		)

      elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
        lowerCamelCase_       	=		dataset.make_dynamic_sampler(
            self.hparams.max_tokens_per_batch			,		distributed=self.hparams.gpus > 1   )
        return DataLoader(
            __SCREAMING_SNAKE_CASE			,		batch_sampler=__SCREAMING_SNAKE_CASE			,		collate_fn=dataset.collate_fn			,		num_workers=self.num_workers			,		)
      else:
        return DataLoader(
            __SCREAMING_SNAKE_CASE			,		batch_size=__SCREAMING_SNAKE_CASE			,		collate_fn=dataset.collate_fn			,		shuffle=__SCREAMING_SNAKE_CASE			,		num_workers=self.num_workers			,		sampler=__SCREAMING_SNAKE_CASE			,		)






    def   UpperCamelCase			(			self	:				Dict   )	->						DataLoader:
      lowerCamelCase_       	=		self.get_dataloader('train'			,		batch_size=self.hparams.train_batch_size			,		shuffle=__SCREAMING_SNAKE_CASE   )
      return dataloader






    def   UpperCamelCase			(			self	:				int   )	->						DataLoader:
      return self.get_dataloader('val'			,		batch_size=self.hparams.eval_batch_size   )






    def   UpperCamelCase			(			self	:				int   )	->						DataLoader:
      return self.get_dataloader('test'			,		batch_size=self.hparams.eval_batch_size   )






    @staticmethod
    def   UpperCamelCase			(			__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						Dict:
      BaseTransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      add_generic_args(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      parser.add_argument(
          '--max_source_length'			,		default=1024			,		type=__SCREAMING_SNAKE_CASE			,		help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )			,		)
      parser.add_argument(
          '--max_target_length'			,		default=56			,		type=__SCREAMING_SNAKE_CASE			,		help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )			,		)
      parser.add_argument(
          '--val_max_target_length'			,		default=142			,		type=__SCREAMING_SNAKE_CASE			,		help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )			,		)
      parser.add_argument(
          '--test_max_target_length'			,		default=142			,		type=__SCREAMING_SNAKE_CASE			,		help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )			,		)
      parser.add_argument('--freeze_encoder'			,		action='store_true'   )
      parser.add_argument('--freeze_embeds'			,		action='store_true'   )
      parser.add_argument('--sortish_sampler'			,		action='store_true'			,		default=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--overwrite_output_dir'			,		action='store_true'			,		default=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--max_tokens_per_batch'			,		type=__SCREAMING_SNAKE_CASE			,		default=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--logger_name'			,		type=__SCREAMING_SNAKE_CASE			,		choices=['default', 'wandb', 'wandb_shared']			,		default='default'   )
      parser.add_argument('--n_train'			,		type=__SCREAMING_SNAKE_CASE			,		default=-1			,		required=__SCREAMING_SNAKE_CASE			,		help='# examples. -1 means use all.'   )
      parser.add_argument('--n_val'			,		type=__SCREAMING_SNAKE_CASE			,		default=500			,		required=__SCREAMING_SNAKE_CASE			,		help='# examples. -1 means use all.'   )
      parser.add_argument('--n_test'			,		type=__SCREAMING_SNAKE_CASE			,		default=-1			,		required=__SCREAMING_SNAKE_CASE			,		help='# examples. -1 means use all.'   )
      parser.add_argument(
          '--task'			,		type=__SCREAMING_SNAKE_CASE			,		default='summarization'			,		required=__SCREAMING_SNAKE_CASE			,		help='# examples. -1 means use all.'   )
      parser.add_argument('--label_smoothing'			,		type=__SCREAMING_SNAKE_CASE			,		default=0.0			,		required=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--src_lang'			,		type=__SCREAMING_SNAKE_CASE			,		default=''			,		required=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--tgt_lang'			,		type=__SCREAMING_SNAKE_CASE			,		default=''			,		required=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--eval_beams'			,		type=__SCREAMING_SNAKE_CASE			,		default=__SCREAMING_SNAKE_CASE			,		required=__SCREAMING_SNAKE_CASE   )
      parser.add_argument(
          '--val_metric'			,		type=__SCREAMING_SNAKE_CASE			,		default=__SCREAMING_SNAKE_CASE			,		required=__SCREAMING_SNAKE_CASE			,		choices=['bleu', 'rouge2', 'loss', None]   )
      parser.add_argument('--eval_max_gen_length'			,		type=__SCREAMING_SNAKE_CASE			,		default=__SCREAMING_SNAKE_CASE			,		help='never generate more than n tokens'   )
      parser.add_argument('--save_top_k'			,		type=__SCREAMING_SNAKE_CASE			,		default=1			,		required=__SCREAMING_SNAKE_CASE			,		help='How many checkpoints to save'   )
      parser.add_argument(
          '--early_stopping_patience'			,		type=__SCREAMING_SNAKE_CASE			,		default=-1			,		required=__SCREAMING_SNAKE_CASE			,		help=(
              '-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
              ' val_check_interval will effect it.'
          )			,		)
      return parser






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Union[str, Any]		=					"""translation"""
    SCREAMING_SNAKE_CASE		:	List[str]		=					["""loss"""]
    SCREAMING_SNAKE_CASE		:	str		=					["""bleu"""]
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					"""bleu"""






    def __init__(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Dict			,		**__SCREAMING_SNAKE_CASE	:				Tuple   )	->						Optional[Any]:
      super().__init__(__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		hparams.src_lang
      lowerCamelCase_       	=		hparams.tgt_lang






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				List[str]   )	->						dict:
      return calculate_bleu(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						Tuple=None					) ->							SummarizationModule:
  Path(args.output_dir					).mkdir(exist_ok=_lowerCamelCase					)
  check_output_dir(_lowerCamelCase						,							expected_items=3					)

  if model is None:
    if "summarization" in args.task:
      lowerCamelCase_       	=		SummarizationModule(_lowerCamelCase					)
    else:
      lowerCamelCase_       	=		TranslationModule(_lowerCamelCase					)
  lowerCamelCase_       	=		Path(args.data_dir					).name
  if (
      args.logger_name == "default"
      or args.fast_dev_run
      or str(args.output_dir					).startswith('/tmp'					)
      or str(args.output_dir					).startswith('/var'					)
  ):
    lowerCamelCase_       	=		True  # don't pollute wandb logs unnecessarily
  elif args.logger_name == "wandb":
    from pytorch_lightning.loggers import WandbLogger

    lowerCamelCase_       	=		os.environ.get('WANDB_PROJECT'						,							_lowerCamelCase					)
    lowerCamelCase_       	=		WandbLogger(name=model.output_dir.name						,							project=_lowerCamelCase					)

  elif args.logger_name == "wandb_shared":
    from pytorch_lightning.loggers import WandbLogger

    lowerCamelCase_       	=		WandbLogger(name=model.output_dir.name						,							project=F'''hf_{dataset}'''					)

  if args.early_stopping_patience >= 0:
    lowerCamelCase_       	=		get_early_stopping_callback(model.val_metric						,							args.early_stopping_patience					)
  else:
    lowerCamelCase_       	=		False

  lowerCamelCase_       	=		args.val_metric == 'loss'
  lowerCamelCase_       	=		generic_train(
      _lowerCamelCase						,							_lowerCamelCase						,							logging_callback=SeqaSeqLoggingCallback()						,							checkpoint_callback=get_checkpoint_callback(
          args.output_dir						,							model.val_metric						,							args.save_top_k						,							_lowerCamelCase					)						,							early_stopping_callback=_lowerCamelCase						,							logger=_lowerCamelCase						,							)
  pickle_save(model.hparams						,							model.output_dir / 'hparams.pkl'					)
  if not args.do_predict:
    return model

  lowerCamelCase_       	=		''
  lowerCamelCase_       	=		sorted(glob.glob(os.path.join(args.output_dir						,							'*.ckpt'					)						,							recursive=_lowerCamelCase					)					)
  if checkpoints:
    lowerCamelCase_       	=		checkpoints[-1]
    lowerCamelCase_       	=		checkpoints[-1]
  trainer.logger.log_hyperparams(model.hparams					)

  # test() without a model tests using the best checkpoint automatically
  trainer.test()
  return model


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE   :							List[str]          =		argparse.ArgumentParser()
  _SCREAMING_SNAKE_CASE   :							Any          =		pl.Trainer.add_argparse_args(parser)
  _SCREAMING_SNAKE_CASE   :							Any          =		SummarizationModule.add_model_specific_args(parser, os.getcwd())

  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		parser.parse_args()

  main(args)




